tbuckley/cabot-search huggingface dataset:
Abstract 5397: Prediction of conversion surgery completion following neoadjuvant modified FOLFIRINOX in borderline resectable and locally advanced pancreatic adenocarcinoma: machine learning algorithm analysis
Authors: Hyunseok Yoon, Kyu-Pyo Kim, Inkeun Park, Jae Ho Jeong, Heung-Moon Chang, Baek-Yeol Ryoo, Changhoon Yoo
Journal: Cancer Research (2023)

Abstract Background: Neoadjuvant modified (m) FOLFIRINOX is a standard therapy for medically fit patients with borderline resectable pancreatic cancer (BRPC) and locally advanced unresectable pancreatic cancer (LAPC). Completion of conversion surgery following mFOLFIRINOX is an important prognostic factor. In this study, we evaluated multiple machine learning models to predict completion of conversion surgery following neoadjuvant mFOLFIRINOX in BRPC and LAPC. Methods: Between January 2017 and December 2020, a total of 647 patients with BRPC and LAPC treated with neoadjuvant mFOLFIRINOX at Asan Medical Center, Seoul, South Korea were enrolled. All demographic and clinicopathological data were retrospectively collected; age, ECOG performance status, BMI, resectability as per the NCCN guidelines, tumor location, tumor size, maximum standardized uptake value assessed by 18F-FDG PET/CT, number of cycles of mFOLFIRINOX, tumor response to mFOLFIRINOX, and serum CA 19-9. Several machine learning models, such as logistic regression and random forest classifier, were trained to predict curative-intent resection and were then internally validated. The relative importance of each variable was analyzed using both Gini importance and permutation importance. Results: Among 647 patients, 173 (26.7%) underwent curative-intent conversion surgery (R0 or R1) following mFOLFIRINOX. The patients who underwent surgery showed significantly better overall survival (median 42.1 vs 17.2 months, P&amp;lt;0.0001) than those who did not. In multivariable analysis using logistic regression, age, resectability as per the NCCN guidelines, tumor size, number of cycles of mFOLFIRINOX, tumor response to mFOLFIRINOX, and CA 19-9 change rate (percentage change in CA 19-9 after mFOLFIRINOX) were significantly correlated to tumor removal with curative-intent conversion surgery. Random forest classifier showed the best predictive capability with AUC at 0.832. The most important feature was the CA 19-9 change rate. Conclusions: In this large cohort-based analysis, completion of conversion surgery could be predicted successfully in BRPC and LAPC patients treated with neoadjuvant mFOLFIRINOX using machine learning models. Further external validation is necessary for generalization. Citation Format: Hyunseok Yoon, Kyu-Pyo Kim, Inkeun Park, Jae Ho Jeong, Heung-Moon Chang, Baek-Yeol Ryoo, Changhoon Yoo. Prediction of conversion surgery completion following neoadjuvant modified FOLFIRINOX in borderline resectable and locally advanced pancreatic adenocarcinoma: machine learning algorithm analysis. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5397.
Abstract 3157: Prediction of adverse drug reactions of anti-breast cancer drugs using machine learning
Authors: Kang Seongmo, Suehyun Lee, Hyunah Shin, Hyun Uk Kim
Journal: Cancer Research (2023)

Abstract Chemotherapy is a representative treatment option for cancers, but one of the challenges during the course of cancer treatment is the possible occurrence of adverse drug reactions (ADRs) caused by an anticancer drug. For example, ADRs such as encephalopathy, radiation recall dermatitis, acute cardiogenic shock and febrile neutropenia have been reported for 5-fluorouracil, an anticancer drug used to treat several cancer types. These ADRs may be caused by a single drug or by interactions with other drugs. Unfortunately, it is extremely difficult to predict potential ADRs a priori because the occurrence of ADR depends on the patient’s genetics and health condition as well as a combination of drugs taken together. To address this problem, we use electric health record (EHR) data of breast cancer patients as a proof-of-concept demonstration to develop a machine learning model that predicts ADRs. For this, administrations of breast cancer patients, diagnostic records, and additional personal information (e.g., age and gender) were used as input data. To deal with high sparsity of EHR data, which has categorical information, factorization machine was applied. Factorization machine can effectively capture key information in an sparse environment. The model classifies whether a patient shows one of pre-defined ADRs as an output. The machine learning model developed in this study can be useful for predicting potential ADRs of various drugs by taking into account a patient’s health condition. Citation Format: Kang Seongmo, Suehyun Lee, Hyunah Shin, Hyun Uk Kim. Prediction of adverse drug reactions of anti-breast cancer drugs using machine learning [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3157.
Abstract 316: Engineering large chromosomal deletions to advance precision oncology for prostate cancer
Authors: Jinjin Wu, Chen Wu, Joel A. Lawitts, Cheryl B. Bock, Jiaoti Huang, Ming Chen
Journal: Cancer Research (2023)

Abstract Human prostate cancer is notable for its high number of somatic copy number alterations (CNAs), which include both focal and broad arm-level amplifications and deletions. Up to 90% of prostate tumors harbor aberrant CNAs, and genomic deletions affect about 40% of these tumors. Many chromosomal deletions are highly recurrent and occur in more than 10% of prostate tumors, including 8p (40~50%), 6q14-16, 10q23, 13q14, and 16q22-24 (20~30% each). Most of these deletions are large, often including hundreds of genes. Notably, several large deletions, including 6q14-16 and 16q22-24, are significantly associated with poor patient outcomes, suggesting that these large chromosomal deletions play a functional role in prostate tumorigenesis, and highlighting the critical need for elucidation of the mechanisms underlying large-deletion driven tumorigenesis. However, due to the limited cloning capacity of targeting vectors and the rarity of on-target homologous recombination events in traditional gene-targeting technology, modelling large chromosomal deletions has proven highly challenging, and the role of such lesions in tumorigenesis has therefore been significantly understudied. Here, we developed an innovative approach to engineering the chromosome over large genetic distances through CRISPR/Cas9 technology in both mouse embryonic stem (ES) cells and human prostate cancer cells. Upon completion of the desired genome edits, the resulting mouse ES clones make possible the creation of conditional knockout mice that accurately mimic prostate cancer-associated large deletions, while the engineered human cancer cell clones serve as an indispensable in vitro model to validate the biological relevance of large chromosomal deletions in prostate tumorigenesis. Further characterization of the human prostate cell line bearing 10q23 deletion revealed that 10q23 loss conferred RWPE1 cells growth advantage. The development and characterization of these in vivo and in vitro models will contribute greatly to the implementation of precision oncology research, open new opportunities for modelling human diseases associated with large chromosomal deletions, and lead to novel insights into the biology of prostate cancer-associated large deletions, which will facilitate the identification of deletion-specific vulnerabilities that can be exploited for prostate cancer treatment. Citation Format: Jinjin Wu, Chen Wu, Joel A. Lawitts, Cheryl B. Bock, Jiaoti Huang, Ming Chen. Engineering large chromosomal deletions to advance precision oncology for prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 316.
Abstract 3101: Discovery of helical sulfonyl-γ-AApeptides targeting LC3 in cancer cells
Authors: Mentalla Mahmoud, Jianyu Chen, Jianfeng Cai, Hao Xie
Journal: Cancer Research (2023)

Abstract Introduction: Autophagy pathway activation and upregulation is an important mechanism for cancer cell survival upon exposure to chemotherapeutic agents. LC3, a ubiquitin-like protein, plays a key role in the regulation of cargo selection and autophagosome formation. Given small molecule inhibitors have not been well developed to target LC3, we aim to design and synthesize an LC3 binder and evaluate its activity in the inhibition of autophagy pathway in cancer cells. Methods: The crystal structure of LC3 and p62 complex (PDB: 2ZJD) was used to assist our design of helical sulfonyl-γ-AApeptides to the LC3 interacting region of p62. The peptidomimetics were synthesized on the solid phase and purified by HPLC. We used fluorescent polarization assay to assess the binding affinity of the peptidomimetics to LC3. We then utilized an LC3 HiBiT reporter cell line (U2OS) from Promega, which was engineered to have a fused human LC3-HiBiT with a HaloTag in between to study the effect of the lead LC3 binder on autophagic flux. In addition, confocal microscopy enabled direction visualization of the colocalization of FITC-labeled LC3 binder with LC3 and lysosome, respectively. Cytotoxicity of LC3 binder was evaluated in colorectal cancer cell line HCT116. Results: We designed and synthesized 6 helical sulfonyl-γ-AApeptides (JC1-6) to target the binding interface of LC3 and p62. These peptidomimetic compounds were designed to retain essential LC3 and p62 interactions at the W and L regions and yet have different functional groups on the helical scaffold. Compounds JC1 and JC6 have binding affinity of Kd 280 nM and 150 nM with LC3, respectively. FITC-labelled JC6 was able to penetrate U2OS and HCT116 cells. It had intracytoplasmic colocalization with not only lysosomes but also LC3 aggregates. Direct visualization of intracytoplasmic LC3 aggregates showed that JC6 at 50 µM significantly inhibited the autophagic degradation of LC3 aggregates. This effect was augmented by the presence of 2 µM autophagy activator PP242. Lastly, JC6 demonstrated cytotoxicity with IC50 approximately 50 µM in HCT116 cells bearing baseline autophagy activation compared to lack of cytotoxicity in U2OS cells with no baseline autophagy activity. Conclusion: We designed and synthesized novel helical sulfonyl-γ-AApeptide LC3 binders. With the ability of cellular penetration, they demonstrated inhibition of autophagic flux and preliminary cytotoxicity in cancer cells. Future studies may focus on structural activity relationship and further evaluation of its mechanisms of action in cancer cells. Citation Format: Mentalla Mahmoud, Jianyu Chen, Jianfeng Cai, Hao Xie. Discovery of helical sulfonyl-γ-AApeptides targeting LC3 in cancer cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3101.
Abstract 5398: Optimization generator app (OpGen) for radiotherapy treatment planning using case-based reasoning
Authors: Reza Reiazi, Surendra Prajapati, Abdallah Sherif Mohamed, Clifton David Fuller, Mohammad Salehpour
Journal: Cancer Research (2023)

Abstract Case-based reasoning (CBR) means adopting previous experiences (i.e, data from previous patients) to meet new demands (new patients). In this scenario, we developed an application named OpGen to generate optimization objective values for head and neck radiotherapy inverse treatment planning, using the previously treated patient with an acceptable outcome. OpGen will be helpful in radiotherapy treatment planning automation and efficiently reducing the required time to reach the target goals and improving the quality and safety of the treatment.OpGen has been developed in Python and has four main modules: 1- Analyzing radiotherapy treatment planning parameters of the previous patients in the database. We have developed (a) a powerful graphical user interface (GUI) that allows quantitative and qualitative analysis of the previous knowledge, including the type of frequently used objective parameter (Min Dose, Max Dose, Min DVH dose, etc.); (b) The range of values used for each specific objective type, and (c) Histogram of the number of patients vs. values used for a specific objective type. 2- Importing patients from treatment planning and retrieving a user-defined number of treatment planning information from the previously treated patients in the database corresponding to the given patient using the user-defined quantitative features (geometrical, distance or geo-distance) and retrieval algorithms (k-Nearest Neighbor and Random Forest). 3- Generating optimization objective values based on the following available options: (a) The most similar patient in the database; (b)- Average or median value over the number of patients retrieved from the database; and (c) Deep learning generated values using a model trained over 100 similar cases retrieved from the database. 4- Generate a portable database of the objective values and patient geometric information for any inter- or intra-institutional usage of the algorithm. OpGen is compatible with RayStation (RaySearch Laboratories™) treatment planning and can retrieve, analyze and generate treatment planning optimization parameters in less than 3 minutes. OpGen can potentially increase the quality and safety of radiotherapy treatment planning by improving the efficiency of the optimization process. Citation Format: Reza Reiazi, Surendra Prajapati, Abdallah Sherif Mohamed, Clifton David Fuller, Mohammad Salehpour. Optimization generator app (OpGen) for radiotherapy treatment planning using case-based reasoning. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5398.
Abstract 3339: Circulating long non-coding RNA HULC as an early indicator of hepatocellular carcinoma irrespective of etiology
Authors: Joseph George, Mutsumi Tsuchishima, Mikihiro Tsutsumi
Journal: Cancer Research (2023)

Abstract Background: Hepatocellular carcinoma (HCC) is the 6th most common cancer worldwide and is highly prevalent in Asian countries. Long noncoding RNAs (lncRNAs) are non-coding transcripts with a size of more than 200 bases in length. Several studies reported that lncRNAs are highly deregulated in HCC and could be used as biomarkers for early diagnosis and prognostic purposes. In the present study, we measured the circulating lncRNA HULC as an early diagnostic marker for HCC with different etiology. Methods: Serum samples were collected from healthy controls, liver cirrhosis (LC), and HCC patients with different etiology and stored at -80°C until assay. Total RNA was extracted using Qiagen RNeasy kit and tested for RNA quantity and purity using NanoDrop spectrophotometer. The RNA extract that contains lncRNA HULC was transcribed into cDNA using a reverse transcription kit. Employing target specific primers for the lncRNA HULC, qRT-PCR was performed with the synthesized cDNA using Qiagen QuantiFast SYBR Green PCR kit on a Rotor-Gene qRT-PCR system. Results: Quantification of lncRNA HULC using qRT-PCR in the serum samples demonstrated significant increase in LC cases and in all HCC cases irrespective of etiology compared to the normal healthy controls. The highest concentration was observed in serum samples from HCC patients with hepatitis B virus (HBV) infection, which was about 8 fold higher compared to healthy controls. There was no significant difference between the mean HULC lncRNA levels in HCC patients with different etiology such as alcoholic cirrhosis, LC due to other reasons, hepatitis B and hepatitis C virus (HCV) infections, and HCC developed from other unknown causes. However, serum HULC lncRNA levels in LC cases without HCC was significantly lower compared to all HCC samples. Conclusions: The results of the present study demonstrated that there is a significant increase in HULC lncRNA levels in the serum samples of all HCC patients irrespective of etiology. Therefore, the circulating lncRNA HULC could be used as an early diagnostic biomarker for HCC before appearing other clinical symptoms. Citation Format: Joseph George, Mutsumi Tsuchishima, Mikihiro Tsutsumi. Circulating long non-coding RNA HULC as an early indicator of hepatocellular carcinoma irrespective of etiology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3339.
Abstract 637: Cytokine CCL9 mediates oncogenic Kras-induced pancreatic acinar-to-ductal metaplasia through ROS
Authors: Geou-Yarh Liou, Justin K. Messex, Kiyah L. Adams
Journal: Cancer Research (2023)

Abstract Pancreatic ductal adenocarcinoma (PDAC) can be originated from acinar-to-ductal metaplasia (ADM). Pancreatic acini harboring oncogenic Kras mutations are transdifferentiated to a duct-like phenotype that further progresses to become pancreatic intraepithelial neoplasia (PanIN) lesions, giving rise to PDAC. Although ADM formation is frequently observed in KrasG12D transgenic mouse models of PDAC, the exact mechanisms of how oncogenic KrasG12D regulates this process remain an enigma. Herein we reveal a new downstream target of oncogenic Kras, cytokine CCL9 during ADM formation. Exogenously treating primary pancreatic acini with recombinant CCL9 in a 3D organoid culture system drives ADM formation. Furthermore, knockdown of CCL9 in KrasG12D-expressed acini reduced KrasG12D-induced ADM. Higher levels of reactive oxygen species were detected in the KrasG12D-expressed pancreatic acini, and knockdown of CCL9 in these acini dramatically decreased ROS levels. Depletion of ROS in 3D organoid culture of CCL9-expressed acini reduced CCL9-induced ADM formation. In transgenic mice of p48cre:KrasG12D, depletion of CCL9 through its specific neutralizing antibody attenuated ADM formation as well as PanIN structures. Hence, our results provided insight into how oncogenic Kras regulates pancreatic ADM through a new downstream target molecule, CCL9. Citation Format: Geou-Yarh Liou, Justin K. Messex, Kiyah L. Adams. Cytokine CCL9 mediates oncogenic Kras-induced pancreatic acinar-to-ductal metaplasia through ROS [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 637.
Abstract 6430: Single-cell transcriptomic and epigenomic landscapes of innate and adaptive immune cells in metastatic melanoma treated with immunotherapy
Authors: Jiekun Yang, Doris Fu, Kyriakitsa Galani, Li-Lun Ho, Emily J. Robitschek, Dennie T. Frederick, Sandeep K. Yadav, Wentao Deng, Anand K. Singh, Kelly P. Burke, Cassia Wang, Tatyana Sharova, David Liu, Kunal Rai, Genevieve M. Boland, Manolis Kellis
Journal: Cancer Research (2023)

Abstract Immune checkpoint inhibitors (ICIs) have revolutionized the care for cancer and extended survival for advanced cancer patients. While ICIs have shown astonishing clinical benefits, less than 50% of patients experience a durable response. To find better biomarkers for ICI response and understand the diverse cellular players in the tumor microenvironment (TME), we performed single-cell RNA sequencing (scRNA; 222,351 cells; 39 samples), single-nucleus transposase-accessible chromatin sequencing (scATAC; 45,478 cells; 15 samples), and bulk RNA sequencing (39 samples) on a metastatic melanoma cohort with rich phenotypic and molecular data. In ICI resistant tumors, we revealed how cell-adhesion and ribosomal activity changes in adaptive immune cells can reflect tumor-level therapeutic failure. We characterized T cell diversity in the TME and discovered an early activated state and a terminally exhausted state. Among the innate immune cells, we detected a mature dendritic cell (DC) state as a strong prognostic predictor of progression-free survival associated with ICI treatment. We studied its differentiation trajectory, transcriptome signatures, epigenome landscape and interactome with T-cells, in comparison to conventional dendritic cell types 1 and 2. We also showed that CRISPR deactivation of the enhancers induced by the mature DC state compromised DCs’ antigen-presentation capabilities and their interactions with antigen-specific T-cells. Lastly, we identified genes and functional modules associated with ICI response (e.g. cell-adhesion) and resistance (e.g. oxidative phosphorylation). Given their functional roles in strengthening immune response to cancer, the molecular and cellular mediators discovered here can help predict ICI regimen efficacy, guide therapeutic development and drug combinations, and pave the path to targeted therapies with long-term effects and new immunotherapies. Citation Format: Jiekun Yang, Doris Fu, Kyriakitsa Galani, Li-Lun Ho, Emily J. Robitschek, Dennie T. Frederick, Sandeep K. Yadav, Wentao Deng, Anand K. Singh, Kelly P. Burke, Cassia Wang, Tatyana Sharova, David Liu, Kunal Rai, Genevieve M. Boland, Manolis Kellis. Single-cell transcriptomic and epigenomic landscapes of innate and adaptive immune cells in metastatic melanoma treated with immunotherapy. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6430.